{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Multi-Label Odor Descriptors using OpenPOM\n",
    "This tutorial will guide you through the process of using DeepChem and OpenPOM to predict multi-label odor descriptors from smell molecules. The tutorial uses concepts of graph neural network for the creation of molecular embeddings.\n",
    "\n",
    "## Prerequisites\n",
    "* Basic knowledge of Python programming.\n",
    "* Familiarity with machine learning concepts.\n",
    "\n",
    "## Table of Contents:\n",
    "* [Introduction](#introduction)\n",
    "* [Principal Odor Map](#pom)\n",
    "* [About OpenPOM](#about_openpom)\n",
    "* [Odor Prediction with OpenPOM](#openpom_pred)\n",
    "    * [Load Dataset](#load-dataset)\n",
    "    * [Feature Extraction](#feature-extraction)\n",
    "    * [Dataset Splits](#dataset-splits)\n",
    "    * [Handling Dataset Imbalance](#handling-dataset-imbalance)\n",
    "    * [Optimial Learning Rate](#optimial-learning-rate)\n",
    "    * [Model Initialization](#model-initialization)\n",
    "    * [Training and Evaluation](#training-and-evaluation)\n",
    "    * [Inference](#inference)\n",
    "* [Visualizing Perceptual Odor Space](#visualize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a class=\"anchor\" name=\"introduction\"></a>\n",
    "Odor molecules are everywhere and it is the nasal apparatus of the animals which acts as the first point of contact with these molecules. The molecules later interact with the receptors and the electrical signals produced are sent to the inner part of the brain for further processing. These signals are differentiated in the brain via various mechanisms. Humans are known to be able to recognise millions of molecules.\n",
    "\n",
    "The task of predicting the odor of molecules is a very interesting challenge mainly due to the paucity of odor molecules and the models. Structurally similar molecules sometimes have similar smells and completely different structures have completely different smells as shown in Fig 1. [[Lee et al. 2022](https://www.biorxiv.org/content/10.1101/2022.09.01.504602v4)]\n",
    "\n",
    "![image.png](./assets/odor_mol_structure.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Odor Map <a class=\"anchor\" name=\"pom\"></a>\n",
    "[The Principal Odor Map (POM) paper by Brian K. Lee et al. (2023)](https://www.science.org/doi/10.1126/science.ade4401) is a significant step towards connecting the molecular structure with how we perceive odors. Specifically, they use graph neural networks to generate  molecular embedding and create a POM that maintains the relationships between odors and allows for predicting the quality of odors that haven't been characterized before.\n",
    "\n",
    "This method has wide-ranging implications, opening the door to predicting odors in general and laying the foundation for the digitalization of scents. It addresses longstanding gaps in our understanding of the sense of smell and brings us closer to a future where odors can be recorded and reproduced. Additionally, it could uncover new scents for industries like fragrance and flavor, potentially reducing the reliance on endangered plants. Moreover, it may lead to the discovery of new functional scents, such as those effective in mosquito repellents or masking unpleasant odors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About OpenPOM <a class=\"anchor\" name=\"about_openpom\"></a>\n",
    "OpenPOM is an open-source replication of the Principal Odor Map paper. This implementation is made such that it easily integrates with Deepchem.\n",
    "\n",
    "Link to the implementation: https://github.com/BioMachineLearning/openpom\n",
    "\n",
    "### Steps to install OpenPOM library:\n",
    "**Step 1:** Install the openpom package from https://pypi.org/project/openpom/ using the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install openpom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Install CUDA version of dgl libraries from https://www.dgl.ai/pages/start.html\n",
    "\n",
    "For cuda 11.7, use the following commands:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install  dgl -f https://data.dgl.ai/wheels/cu117/repo.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install  dglgo -f https://data.dgl.ai/wheels-test/repo.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odor Prediction with OpenPOM <a class=\"anchor\" name=\"openpom_pred\"></a>\n",
    "\n",
    "Import necessary libraries and classes:\n",
    " - Deepchem\n",
    " - RDKIT\n",
    " - Pandas\n",
    " - Featurizer and utils from openpom\n",
    " - Main GCN model from openpom\n",
    " - Others\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import deepchem as dc\n",
    "from rdkit import Chem\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from openpom.feat.graph_featurizer import GraphFeaturizer, GraphConvConstants\n",
    "from openpom.utils.data_utils import get_class_imbalance_ratio, IterativeStratifiedSplitter\n",
    "from openpom.models.mpnn_pom import MPNNPOMModel\n",
    "from datetime import datetime\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from scipy.stats import gaussian_kde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describing smells involves associating words with the sensations experienced when inhaling an aromatic substance. While there's a wide range of terms available for this task, not all of them are specific. It's feasible to categorize words into groups that are frequently used together when capturing the essence of a scent.\n",
    "\n",
    "Tasks (labels) for this tutorial are all 138 odor descriptors used in the Principal Odor Map paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of tasks:  138\n"
     ]
    }
   ],
   "source": [
    "TASKS = [\n",
    "'alcoholic', 'aldehydic', 'alliaceous', 'almond', 'amber', 'animal',\n",
    "'anisic', 'apple', 'apricot', 'aromatic', 'balsamic', 'banana', 'beefy',\n",
    "'bergamot', 'berry', 'bitter', 'black currant', 'brandy', 'burnt',\n",
    "'buttery', 'cabbage', 'camphoreous', 'caramellic', 'cedar', 'celery',\n",
    "'chamomile', 'cheesy', 'cherry', 'chocolate', 'cinnamon', 'citrus', 'clean',\n",
    "'clove', 'cocoa', 'coconut', 'coffee', 'cognac', 'cooked', 'cooling',\n",
    "'cortex', 'coumarinic', 'creamy', 'cucumber', 'dairy', 'dry', 'earthy',\n",
    "'ethereal', 'fatty', 'fermented', 'fishy', 'floral', 'fresh', 'fruit skin',\n",
    "'fruity', 'garlic', 'gassy', 'geranium', 'grape', 'grapefruit', 'grassy',\n",
    "'green', 'hawthorn', 'hay', 'hazelnut', 'herbal', 'honey', 'hyacinth',\n",
    "'jasmin', 'juicy', 'ketonic', 'lactonic', 'lavender', 'leafy', 'leathery',\n",
    "'lemon', 'lily', 'malty', 'meaty', 'medicinal', 'melon', 'metallic',\n",
    "'milky', 'mint', 'muguet', 'mushroom', 'musk', 'musty', 'natural', 'nutty',\n",
    "'odorless', 'oily', 'onion', 'orange', 'orangeflower', 'orris', 'ozone',\n",
    "'peach', 'pear', 'phenolic', 'pine', 'pineapple', 'plum', 'popcorn',\n",
    "'potato', 'powdery', 'pungent', 'radish', 'raspberry', 'ripe', 'roasted',\n",
    "'rose', 'rummy', 'sandalwood', 'savory', 'sharp', 'smoky', 'soapy',\n",
    "'solvent', 'sour', 'spicy', 'strawberry', 'sulfurous', 'sweaty', 'sweet',\n",
    "'tea', 'terpenic', 'tobacco', 'tomato', 'tropical', 'vanilla', 'vegetable',\n",
    "'vetiver', 'violet', 'warm', 'waxy', 'weedy', 'winey', 'woody'\n",
    "]\n",
    "\n",
    "print(\"No of tasks: \", len(TASKS))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset\n",
    "\n",
    "The dataset that we will be using for this tutorial is a curated multi-labelled SMILES odor dataset with 138 odor descriptors. This dataset was curated based on the steps suggested in the Principal Odor Map paper.\n",
    "\n",
    "It contains **4983** molecules, each described by multiple odor labels (e.g. creamy, grassy), made by combining the **GoodScents** and **Leffingwell PMP 2001** datasets each containing odorant molecules and corresponding odor descriptors.\n",
    "\n",
    "The steps for making the dataset can be found in the notebooks here: https://github.com/BioMachineLearning/openpom/tree/main/openpom/data.\n",
    "\n",
    "This dataset can be downloaded from any of the following links:\n",
    "1. https://raw.githubusercontent.com/ARY2260/openpom/main/openpom/data/curated_datasets/curated_GS_LF_merged_4983.csv\n",
    "2. https://www.kaggle.com/datasets/aryanamitbarsainyan/multi-labelled-smiles-odors-dataset/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-11-22 16:16:54--  https://raw.githubusercontent.com/ARY2260/openpom/main/openpom/data/curated_datasets/curated_GS_LF_merged_4983.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1647971 (1.6M) [text/plain]\n",
      "Saving to: ‘curated_GS_LF_merged_4983.csv’\n",
      "\n",
      "curated_GS_LF_merge 100%[===================>]   1.57M  8.48MB/s    in 0.2s    \n",
      "\n",
      "2023-11-22 16:16:55 (8.48 MB/s) - ‘curated_GS_LF_merged_4983.csv’ saved [1647971/1647971]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# download curated dataset by uncommenting the command below:\n",
    "!wget https://raw.githubusercontent.com/ARY2260/openpom/main/openpom/data/curated_datasets/curated_GS_LF_merged_4983.csv\n",
    "\n",
    "input_file = 'curated_GS_LF_merged_4983.csv' # or new downloaded file path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction\n",
    "\n",
    "Dataset contains odorants represented as SMILES. These SMILES can be analysed using the [RDKit](https://github.com/rdkit/rdkit) to get information about the atoms and bonds in the molecules.\n",
    "\n",
    "Molecular fingerprinting is a vectorized representation of molecules capturing precise details of atomic configurations. During the featurization process, a molecule is decomposed into substructures (e.g., fragments) of a fixed-length binary fingerprint assembled into an array whose each element is either 1 or 0.\n",
    "\n",
    "Featurizer used by: `GraphFeaturizer()` (built on Deepchem Featurizer base class)\n",
    "\n",
    "Features used by authors of Principal Odor Map paper:\n",
    "- atomic features:\n",
    "    - valence\n",
    "    - degree\n",
    "    - hydrogen count\n",
    "    - hybridization\n",
    "    - formal charge\n",
    "    - atomic number\n",
    "- bond features\n",
    "    - degree\n",
    "    - aromaticity\n",
    "    - whether it is in a ring\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurizer = GraphFeaturizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `CSVLoader` from Deepchem to convert the dataset from csv type to deepchem diskdataset format that can be directly used by the model. The loader automatically featurizes the dataset based on the given featurizer.\n",
    "\n",
    "The featurization process automatically handles any salts or organometallics that are present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_field = 'nonStereoSMILES' # column that contains SMILES\n",
    "loader = dc.data.CSVLoader(tasks=TASKS,\n",
    "feature_field=smiles_field,\n",
    "featurizer=featurizer)\n",
    "dataset = loader.create_dataset(inputs=[input_file])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store number of tasks (odor descriptors) and check the length of the dataset after featurization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4983"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_tasks = len(dataset.tasks)\n",
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Splits\n",
    "\n",
    "For splitting the dataset into train, valid and test splits, stratified splitting technique is used because our multi-label dataset has highly unbalanced labels and stratification attempts to preserve many-order label ratios. [Tarekegn A.N. et al. \"A review of methods for imbalanced multi-label classification\" Pattern Recognit. (2021)]()\n",
    "\n",
    "In this tutorial we use `RandomStratifiedSplitter` from deepchem but one can use `IterativeStratifiedSplitter` from openpom utils, which was used in the Principal Odor Map paper.\n",
    "\n",
    "Iterative stratification is a procedure for stratified sampling that attempts to preserve many-order label ratios, prioritizing more unbalanced combinations. For second order, this means preserving ratios of pairs of labels in each split.\n",
    "\n",
    "Splitting ratio: 80:10:10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_dataset:  3999\n",
      "valid_dataset:  498\n",
      "test_dataset:  486\n"
     ]
    }
   ],
   "source": [
    "# get train valid test splits\n",
    "\n",
    "randomstratifiedsplitter = dc.splits.RandomStratifiedSplitter()\n",
    "train_dataset, test_dataset, valid_dataset = randomstratifiedsplitter.train_valid_test_split(dataset, frac_train = 0.8, frac_valid = 0.1, frac_test = 0.1, seed = 1)\n",
    "\n",
    "print(\"train_dataset: \", len(train_dataset))\n",
    "print(\"valid_dataset: \", len(valid_dataset))\n",
    "print(\"test_dataset: \", len(test_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Dataset Imbalance\n",
    "\n",
    "**Imbalance ratio per label (IRLbl)**: Let M be an MLD with a set of labels L and Yi be the label-set of the ith instance. IRLbl is calcu-lated for the label λ as the ratio between the majority label and the label λ, where IRLbl is 1 for the most frequent label and a greater value for the rest. The larger the value of IRLbl, the higher the imbalance level for the concerned label.\n",
    "\n",
    "Reference: [TarekegnA.N. et al. \"A review of methods for imbalanced multi-label classification\" Pattern Recognit. (2021)]()\n",
    "\n",
    "We calculate class imbalance ratios to get each descriptor’s contribution to the loss being weighted by a factor of **log(1+ class_imbalance_ratio)**,\n",
    "This ensured  that rarer descriptors were given a higher weighting. (Based on Principal Odor Map paper)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ratios = get_class_imbalance_ratio(train_dataset)\n",
    "assert len(train_ratios) == n_tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimial Learning Rate\n",
    "\n",
    "We use an **exponential decay** learning rate schedule where we decay the learning rate with more iterations using an exponential function. The inclusion of decay in the learning process helps stabilize it, preventing oscillations. This is crucial as a high constant learning rate can lead to the learning bouncing back and forth across a minimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = dc.models.optimizers.ExponentialDecay(initial_rate=0.001, decay_rate=0.5, decay_steps=32*20, staircase=True)\n",
    "# learning_rate = 0.001 # If you want to use constant lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Initialization\n",
    "\n",
    "`MPNNPOMModel` is the message passing neural network model that we will be using for this tutorial. Its architecture is based on the Principal Odor Map paper.\n",
    "\n",
    "Model architecture:\n",
    "- Message passing layers\n",
    "- Readout layer (set2set or global sum pooling based)\n",
    "- Feed-Forward layer (where the Penultimate layer contains the embeddings and the final layer is sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize model\n",
    "\n",
    "model = MPNNPOMModel(n_tasks = n_tasks,\n",
    "                     batch_size = 128,\n",
    "                     learning_rate = learning_rate,\n",
    "                     class_imbalance_ratio = train_ratios,\n",
    "                     loss_aggr_type = 'sum',\n",
    "                     node_out_feats = 100,\n",
    "                     edge_hidden_feats = 75,\n",
    "                     edge_out_feats = 100,\n",
    "                     num_step_message_passing = 5,\n",
    "                     mpnn_residual = True,\n",
    "                     message_aggregator_type = 'sum',\n",
    "                     mode = 'classification',\n",
    "                     number_atom_features = GraphConvConstants.ATOM_FDIM,\n",
    "                     number_bond_features = GraphConvConstants.BOND_FDIM,\n",
    "                     n_classes = 1,\n",
    "                     readout_type = 'set2set',\n",
    "                     num_step_set2set = 3,\n",
    "                     num_layer_set2set = 2,\n",
    "                     ffn_hidden_list = [392, 392],\n",
    "                     ffn_embeddings = 256,\n",
    "                     ffn_activation = 'relu',\n",
    "                     ffn_dropout_p = 0.12,\n",
    "                     ffn_dropout_at_input_no_act = False,\n",
    "                     weight_decay = 1e-5,\n",
    "                     self_loop = False,\n",
    "                     optimizer_name = 'adam',\n",
    "                     log_frequency = 32,\n",
    "                     model_dir = './experiments',\n",
    "                     device_name ='cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the metric for model performance evaluation to ROC-AUC score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = dc.metrics.Metric(dc.metrics.roc_auc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Evaluation\n",
    "\n",
    "For model training, we set the number of epochs to **30 (empirical)**. Here in this training loop, for each epoch the model is trained on the train split, and the train and valid metric (ROC-AUC) scores are calculated and displayed for that epoch. Observing the train and valid metric score per epoch helps us determine if the model is overfitting for the given hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1/30 ; loss = 3.956435441970825; train_scores = 0.6374192135948238; valid_scores = 0.6303769299701434\n",
      "epoch 2/30 ; loss = 3.217766046524048; train_scores = 0.6894526043415731; valid_scores = 0.6893453634277539\n",
      "epoch 3/30 ; loss = 2.986715316772461; train_scores = 0.7690087502046365; valid_scores = 0.7701695818345347\n",
      "epoch 4/30 ; loss = 2.8684182167053223; train_scores = 0.8115981852648393; valid_scores = 0.801291043683135\n",
      "epoch 5/30 ; loss = 2.8043434619903564; train_scores = 0.8260160404172175; valid_scores = 0.8066394857358837\n",
      "epoch 6/30 ; loss = 2.7285211086273193; train_scores = 0.84573743780417; valid_scores = 0.8249753718498863\n",
      "epoch 7/30 ; loss = 2.6865124702453613; train_scores = 0.8550609387154966; valid_scores = 0.8339593793800096\n",
      "epoch 8/30 ; loss = 2.654895782470703; train_scores = 0.851712587301288; valid_scores = 0.8310866766580537\n",
      "epoch 9/30 ; loss = 2.6008336544036865; train_scores = 0.8631575928448904; valid_scores = 0.8393934890544952\n",
      "epoch 10/30 ; loss = 2.5609755516052246; train_scores = 0.8698679071614784; valid_scores = 0.8456073207705405\n",
      "epoch 11/30 ; loss = 2.534403085708618; train_scores = 0.8678375982660103; valid_scores = 0.8468235838953687\n",
      "epoch 12/30 ; loss = 2.515956163406372; train_scores = 0.8782393133980957; valid_scores = 0.8510788212688803\n",
      "epoch 13/30 ; loss = 2.4854485988616943; train_scores = 0.8713314059538569; valid_scores = 0.8474436479270416\n",
      "epoch 14/30 ; loss = 2.470993757247925; train_scores = 0.8716043572813807; valid_scores = 0.8426277505506767\n",
      "epoch 15/30 ; loss = 2.435399293899536; train_scores = 0.8851761362317436; valid_scores = 0.854211883413371\n",
      "epoch 16/30 ; loss = 2.4035449028015137; train_scores = 0.8918126388806314; valid_scores = 0.8598879530874546\n",
      "epoch 17/30 ; loss = 2.387251138687134; train_scores = 0.893788827574891; valid_scores = 0.8617038507039871\n",
      "epoch 18/30 ; loss = 2.3640966415405273; train_scores = 0.8980127541045434; valid_scores = 0.8662492771154428\n",
      "epoch 19/30 ; loss = 2.3496320247650146; train_scores = 0.9041004860667025; valid_scores = 0.867381857823018\n",
      "epoch 20/30 ; loss = 2.320573568344116; train_scores = 0.9067870561316762; valid_scores = 0.8696829947426502\n",
      "epoch 21/30 ; loss = 2.2593977451324463; train_scores = 0.9150111863442378; valid_scores = 0.8733496081064185\n",
      "epoch 22/30 ; loss = 2.2161407470703125; train_scores = 0.9128967618445943; valid_scores = 0.8718253606375608\n",
      "epoch 23/30 ; loss = 2.2134556770324707; train_scores = 0.9167124057524435; valid_scores = 0.8771245063173232\n",
      "epoch 24/30 ; loss = 2.2012124061584473; train_scores = 0.9187576980084228; valid_scores = 0.8777288573718308\n",
      "epoch 25/30 ; loss = 2.1737425327301025; train_scores = 0.9196310563689644; valid_scores = 0.876801413224764\n",
      "epoch 26/30 ; loss = 2.1538891792297363; train_scores = 0.9194531410182157; valid_scores = 0.8771179370096795\n",
      "epoch 27/30 ; loss = 2.155555009841919; train_scores = 0.9233371750197178; valid_scores = 0.8778147357598621\n",
      "epoch 28/30 ; loss = 2.133833885192871; train_scores = 0.9229875164143767; valid_scores = 0.876982364162354\n",
      "epoch 29/30 ; loss = 2.1264352798461914; train_scores = 0.917759693241235; valid_scores = 0.8759897729950754\n",
      "epoch 30/30 ; loss = 2.1117615699768066; train_scores = 0.9257473041638218; valid_scores = 0.8774277513255287\n"
     ]
    }
   ],
   "source": [
    "nb_epoch = 30\n",
    "start_time = datetime.now()\n",
    "for epoch in range(1, nb_epoch+1):\n",
    "       loss = model.fit(\n",
    "             train_dataset,\n",
    "             nb_epoch=1,\n",
    "             max_checkpoints_to_keep=1,\n",
    "             deterministic=False,\n",
    "             restore=epoch>1)\n",
    "       train_scores = model.evaluate(train_dataset, [metric])['roc_auc_score']\n",
    "       valid_scores = model.evaluate(valid_dataset, [metric])['roc_auc_score']\n",
    "       print(f\"epoch {epoch}/{nb_epoch} ; loss = {loss}; train_scores = {train_scores}; valid_scores = {valid_scores}\")\n",
    "model.save_checkpoint()\n",
    "end_time = datetime.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training, we evaluate the model on the test split to get the model performance on the unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_taken:  0:02:31.613800\n",
      "test_score:  0.8632583362829005\n"
     ]
    }
   ],
   "source": [
    "test_scores = model.evaluate(test_dataset, [metric])['roc_auc_score']\n",
    "print(\"time_taken: \", str(end_time-start_time))\n",
    "print(\"test_score: \", test_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference\n",
    "\n",
    "Let's choose a SMILES of the compound `CCCC(=O)O` (Butyric Acid) to test inference using our trained model. Its odor descriptors are expected to be `'fruity, sour, dairy, sharp, cheesy, buttery'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7efdfcf559e0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_SMILES = 'CCCC(=O)O'\n",
    "Expected_odors = 'fruity, sour, dairy, sharp, cheesy, buttery'\n",
    "\n",
    "mol = Chem.MolFromSmiles(Test_SMILES)\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select the GraphFeaturizer() to featurize the SMILES and use the model to get predictions. Then we plot a bar graph to see the top 10 predicted odors for the SMILES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='odors'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Featuize test smiles\n",
    "featurizer = GraphFeaturizer()\n",
    "featurized_data = featurizer.featurize(Test_SMILES)\n",
    "\n",
    "# Get predictions from trained model\n",
    "prediction = model.predict(dc.data.NumpyDataset(featurized_data))\n",
    "\n",
    "# Visualize the top 10 predictions\n",
    "prediction_df = pd.DataFrame({'odors': TASKS, 'prediction': prediction.squeeze()}).sort_values(by='prediction', ascending=False)\n",
    "prediction_df[:10].plot.bar(x='odors', y='prediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Perceptual Odor Space <a class=\"anchor\" id=\"visualize\"></a>\n",
    "Now we will plot Kernel Density Estimations of select odor categories in a reduced space of embeddings from the Penultimate layer (Principal Odor Map) of the model.\n",
    "\n",
    "We use the trained model to predict embeddings for the dataset. Then we apply PCA to reduce the dimensionality of the embeddings to 2 components. Next we compute KDE for different labels in the dataset (either based on true values or predicted values) and plot contours in the reduced 2D space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pom_plot(model, dataset, is_preds=False):\n",
    "   pom_embeds = model.predict_embedding(dataset)\n",
    "   required_desc = list(dataset.tasks)\n",
    "\n",
    "\n",
    "   # Define type dictionaries for odor categories to be visualized\n",
    "   type1 = {'floral': '#F3F1F7', 'subs': {'muguet': '#FAD7E6', 'lavender': '#8883BE', 'jasmin': '#BD81B7'}}\n",
    "   type2 = {'meaty': '#F5EBE8', 'subs': {'savory': '#FBB360', 'beefy': '#7B382A', 'roasted': '#F7A69E'}}\n",
    "   type3 = {'ethereal': '#F2F6EC', 'subs': {'cognac': '#BCE2D2', 'fermented': '#79944F', 'alcoholic': '#C2DA8F'}}\n",
    "  \n",
    "   # Perform Principal Component Analysis (PCA) to reduce the dimensionality of the embeddings to 2 components\n",
    "   pca = PCA(n_components=2, iterated_power=10)\n",
    "   reduced_features = pca.fit_transform(pom_embeds)\n",
    "\n",
    "\n",
    "   # Get the variance explained by the first two principal components\n",
    "   variance_explained = pca.explained_variance_ratio_\n",
    "   variance_pc1 = variance_explained[0]\n",
    "   variance_pc2 = variance_explained[1]\n",
    "\n",
    "\n",
    "   # If is_preds is True, use the model to make predictions on the dataset\n",
    "   if is_preds:\n",
    "       y_preds = model.predict(dataset)\n",
    "       # Set a threshold for predictions\n",
    "       threshold = np.percentile(y_preds, 95, axis=0)\n",
    "       y = (y_preds >= threshold).astype(int)\n",
    "   else:\n",
    "       # Otherwise, use the true labels from the dataset\n",
    "       y = dataset.y\n",
    "\n",
    "\n",
    "   # Define a grid of points for Kernel Density Estimation (KDE)\n",
    "   x_grid, y_grid = np.meshgrid(np.linspace(reduced_features[:, 0].min(), reduced_features[:, 0].max(), 500),\n",
    "                                np.linspace(reduced_features[:, 1].min(), reduced_features[:, 1].max(), 500))\n",
    "   grid_points = np.vstack([x_grid.ravel(), y_grid.ravel()])\n",
    "\n",
    "\n",
    "   # Define a function to get KDE values for a specific label\n",
    "   def get_kde_values(label):\n",
    "       plot_idx = required_desc.index(label)\n",
    "       label_indices = np.where(y[:, plot_idx] == 1)[0]\n",
    "       kde_label = gaussian_kde(reduced_features[label_indices].T)\n",
    "       kde_values_label = kde_label(grid_points)\n",
    "       kde_values_label = kde_values_label.reshape(x_grid.shape)\n",
    "       return kde_values_label\n",
    "  \n",
    "   # Define a function to plot contours for a given type dictionary\n",
    "   def plot_contours(type_dictionary, bbox_to_anchor):\n",
    "       main_label = list(type_dictionary.keys())[0]\n",
    "       plt.contourf(x_grid, y_grid, get_kde_values(main_label), levels=1, colors=['#00000000',type_dictionary[main_label],type_dictionary[main_label]])\n",
    "       legend_elements = []\n",
    "       for label, color in type_dictionary['subs'].items():\n",
    "           plt.contour(x_grid, y_grid, get_kde_values(label), levels=1, colors=color, linewidths=2)\n",
    "           legend_elements.append(Patch(facecolor=color, label=label))\n",
    "       legend = plt.legend(handles=legend_elements, title=main_label, bbox_to_anchor=bbox_to_anchor)\n",
    "       legend.get_frame().set_facecolor(type_dictionary[main_label])\n",
    "       plt.gca().add_artist(legend)\n",
    "\n",
    "\n",
    "   # Create a figure and plot contours for different types\n",
    "   plt.figure(figsize=(15, 10))\n",
    "   plt.title('KDE Density Estimation with Contours in Reduced Space')\n",
    "   plt.xlabel(f'Principal Component 1 ({round(variance_pc1*100, ndigits=2)}%)')\n",
    "   plt.ylabel(f'Principal Component 2 ({round(variance_pc2*100, ndigits=2)}%)')\n",
    "   plot_contours(type_dictionary=type1, bbox_to_anchor = (0.2, 0.8))\n",
    "   plot_contours(type_dictionary=type2, bbox_to_anchor = (0.9, 0.4))\n",
    "   plot_contours(type_dictionary=type3, bbox_to_anchor = (0.3, 0.1))\n",
    "\n",
    "\n",
    "   # Display the plot\n",
    "   plt.show()\n",
    "   plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotted contours are color-coded based on different categories and their subcategories. Regions abundant with molecules broadly categorized as floral, meaty, or alcoholic are shaded, while regions abundant with more specific category labels (like: lavender, roasted etc.) are outlined.\n",
    "\n",
    "KDE contour plot for true labels (true perceptual odor space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pom_plot(model, dataset, is_preds=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KDE contour plot for predicted labels (predicted perceptual odor space)\n",
    "Note how closely it resembles the true perceptual odor space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pom_plot(model, dataset, is_preds=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Citing This Tutorial <a class=\"anchor\" id=\"cite\"></a>\n",
    "\n",
    "This tutorial was contributed to Deepchem by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@manual{Olfaction,\n",
    "title={Predict Multi-Label Odor Descriptors using OpenPOM},\n",
    "organization={OpenPOM},\n",
    "author={Aryan Amit Barsainyan, Ritesh Kumar, Pinaki Saha, Michael Schmuker},\n",
    "howpublished = {\\url{https://github.com/deepchem/deepchem/blob/master/examples/tutorials/Predict_Multi_Label_Odor_Descriptors_using_OpenPOM.ipynb}},\n",
    "year={2023},\n",
    "}"
   ]
  }
 ],
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